Reading source code is not really doable once you hit the tens of thousands of lines of code. It's even more hopeless at millions of them. Yet, analysis tools that can summarise this information struggle just as much as humans do. So how do we build tools that can handle such ginormous codebases, anyway?
In this talk we'll take a practical (but superficial) look at some of the algorithms involved in the making of Glass, a static analysis tool developed at Klarna, and the optimisations that allow providing answers to analysis in real-time for IDEs, and reasonable-time for build/CI tools.
Quildreen Motta Ribeiro
Quil is an illustrator, writer, and software engineer with a background in programming language design. Although Quil has only been working with Erlang for roughly a year, they've written programs in Clojure, F#, Haskell, and other typed (and not-so-typed) functional languages for a few years. Quil is particularly interested in making software more secure and more reliable, and that has been one of their main personal goals at Klarna. In their spare time, Quil is working on Purr, an environment that aims to make programming more secure (with object-capability security), practical (with algebraic effects), and tangible (with live-feedback).